Multiomics integrative analysis identifies APOE allele-specific blood biomarkers associated to Alzheimer’s disease etiopathogenesis

www.aging-us.com 9277 AGING www.aging-us.com AGING 2021, Vol. 13, No. 7 Research Paper Multiomics integrative analysis identifies APOE allele-specific blood biomarkers associated to Alzheimer’s disease etiopathogenesis Laura Madrid1, Sonia Moreno-Grau2,3, Shahzad Ahmad4, Antonio González-Pérez1, Itziar de Rojas2,3, Rui Xia5, Pamela V. Martino Adami6, Pablo García-González2, Luca Kleineidam6,7,8, Qiong Yang9, Vincent Damotte10, Joshua C. Bis11, Fuensanta Noguera-Perea12, Céline Bellenguez10, Xueqiu Jian5, Juan Marín-Muñoz12, Benjamin Grenier-Boley10, Adela Orellana2,3, M. Arfan Ikram4, Philippe Amouyel10, Claudia L. Satizabal13,14, Alzheimer’s Disease Neuroimaging Initiative (ADNI)*, EADI consortium, CHARGE consortium, GERAD consortium, GR@ACE/DEGESCO consortium, Luis Miguel Real15,16, Carmen Antúnez-Almagro12, Anita DeStefano7,14, Alfredo Cabrera-Socorro17, Rebecca Sims18, Cornelia M. Van Duijn4, Eric Boerwinkle19, Alfredo Ramírez6,7,8, Myriam Fornage5, Jean-Charles Lambert10, Julie Williams18,20, Sudha Seshadri13,14, ADAPTED consortium, Janina S. Ried21, Agustín Ruiz2,3, Maria Eugenia Saez1 1Andalusion Bioiformatics Research Centre (CAEBi), Sevilla, Spain 2Research Center and Memory Clinic Fundació ACE, Institut Català de Neurociències Aplicades, Universitat Internacional de Catalunya, Barcelona, Spain 3CIBERNED, Network Center for Biomedical Research in Neurodegenerative Diseases, National Institute of Health Carlos III, Madrid, Spain 4Department of Epidemiology, Erasmus Medical Centre, Rotterdam, The Netherlands 5Institute of Molecular Medicine and Human Genetics Center, University of Texas Health Science Center at Houston, Houston, TX 77030, USA 6Division of Neurogenetics and Molecular Psychiatry, Department of Psychiatry and Psychotherapy, Medical Faculty, University of Cologne, Cologne, Germany 7Department of Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany 8German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany 9Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA 10University Lille, Inserm, CHU Lille, Institut Pasteur de Lille, U1167-RID-AGE-Facteurs de Risque Et Déterminants Moléculaires des Maladies Liées au Vieillissement, Lille, France 11Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA 98195, USA 12Unidad de Demencias, Hospital Clínico Universitario Virgen de la Arrixaca, Carretera de Madrid-Cartagena s/n, 30120 El Palmar, Murcia, España 13Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, TX 78229, USA 14Department of Neurology, Boston University School of Medicine, Boston, MA 02118, USA 15Unit of Infectious Diseases and Microbiology, Hospital Universitario de Valme, Sevilla, Spain 16Department of Surgery, Biochemistry and Immunology, University of Malaga, Spain 17Janssen Research and Development, a Division of Janssen Pharmaceutica N.V., Beerse, Belgium 18Division of Psychological Medicine and Clinical Neuroscience, School of Medicine, Cardiff University, Cardiff, UK 19Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA 20UKDRI@Cardiff, School of Medicine, Cardiff University, Cardiff, UK 21AbbVie Deutschland GmbH & Co. KG, Genomics Research Center, Knollstrasse, Ludwigshafen, Germany www.aging-us.com 9278 AGING INTRODUCTION Non-Mendelian Alzheimer’s disease (AD) has become the paradigm of a complex disease for which a major genetic determinant is known, the APOE locus. Three linkage studies published in 1993 pointed to the APOE region at 19q13 as a risk locus for late onset familial AD [1, 2], and even common sporadic late-onset AD (LOAD) [3]. Shortly after, researchers around the world confirmed the association of APOE gene with diverse forms of the disease and its association with other dementias. The APOE gene encodes a lipoprotein firstly identified in the 1970s among patients with familial hypercholesterolemia type III [4, 5]. The protein has three major isoforms depending on the combination of two polymorphisms located at positions 112 (rs429358 (C > T)) and 158 (rs7412 (C > T). The most common isoform, APOE3, has a cysteine at position 112 and an arginine at position 158, whereas APOE2, the least common isoform, has a cysteine at both positions, and the AD risk allele APOE4 has an arginine at both positions [6–8]. These aminoacidic substitutions result in a conformational change that brings together the N- terminal and C-terminal domains in APOE4, which are normally separated in APOE2 and APOE3 isoforms. The consequences in downstream signaling of this conformational shift in the APOE4 isoform are still unknown. In fact, it is not even clear if the APOE4 is a gain or loss of function mutation despite extensive research in the field [9]. What is already known is that having a single APOE4 allele increases risk 2- to 4-fold and having two APOE4 alleles increases risk about 8- to 12-fold, although risk varies according to genetic background and sex [10]. In the last years, genome-wide association studies (GWAS) have contributed a number of Alzheimer’s disease associated low penetrance genes, including ABCA7, ABI3, ACE, AC074212.3, ADAM10, ADAMTS1, ADAMTS4, ALPK2, ANKDR31, APH1B, ATP5H, BIN1, BZRAP1-AS1, CASS4, CD2AP, CD33, CELF1-MADD, CLNK, CLU, CNTNAP2, CR1, DSG2, ECHDC3, EPHA1, FERMT2, HESX1, HLA-DRB5– HLA-DRB1, HS3ST1, KAT8, IQCK, INPP5D, NME8, *Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wpcontent/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf Correspondence to: Maria Eugenia Saez; email: m[email protected] Keywords: Alzheimer’s disease, APOE, integrative analysis, biomarkers Received: February 3, 2021 Accepted: March 26, 2021 Published: April 12, 2021 Copyright: © 2021 Madrid et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. ABSTRACT Alzheimer’s disease (AD) is the most common form of dementia, currently affecting 35 million people worldwide. Apolipoprotein E (APOE) ε4 allele is the major risk factor for sporadic, late-onset AD (LOAD), which comprises over 95% of AD cases, increasing the risk of AD 4-12 fold. Despite this, the role of APOE in AD pathogenesis is still a mystery. Aiming for a better understanding of APOE-specific effects, the ADAPTED consortium analyzed and integrated publicly available data of multiple OMICS technologies from both plasma and brain stratified by APOE haplotype (APOE2, APOE3 and APOE4). Combining genome-wide association studies (GWAS) with differential mRNA and protein expression analyses and single-nuclei transcriptomics, we identified genes and pathways contributing to AD in both APOE dependent and independent fashion. Interestingly, we characterized a set of biomarkers showing plasma and brain consistent protein profiles and opposite trends in APOE2 and APOE4 AD cases that could constitute screening tools for a disease that lacks specific blood biomarkers. Beside the identification of APOE-specific signatures, our findings advocate that this novel approach, based on the concordance across OMIC layers and tissues, is an effective strategy for overcoming the limitations of often underpowered single-OMICS studies. www.aging-us.com 9279 AGING NYAP1, MS4A gene cluster, NDUFAF6, OARD1, PICALM, PLCG2, PTK2B, SCIMP, SLC24A4, SORL1, SPI1, TREM2, WWOX, ZCWPW1 [11–14]. Some reports have performed stratified analyses based on the presence or absence of the APOE4 allele, identifying some additional genes with effect in APOE4 carriers (such as ISYNA1, CUGBP2) or in individuals lacking the E4 allele (AC099552, GPAA1, MAPT, NSF, TREM2) [15–18]. One aim of the ADAPTED consortium is to identify specific APOE signatures associated with the different APOE isoforms. We describe for the first time herewith a comprehensive integration of genomic, transcriptomic and proteomic data stratified by the three major APOE haplotypes. RESULTS GWAS data: SNP-level analysis The combined analysis of the three stages (stage I+II+III) (Figure 1 and Supplementary Tables 1–3 and Supplementary Figures 1–3), identified genome-wide significant signals (p<5x10-8) for APOE, BIN1, CLU, CNTNAP2 and PICALM in the APOE4 stratum; suggestive signals (p<10-5) in this analysis include a 1.4Mb intergenic region on 4p15. (from 33.3Mb to 34.7 Mb, hg19) with lowest p value for the SNP rs12641122 (p=6.28x10-7), a 4.5Kb intergenic region on 4q35.2 or the KCNQ3 gene among others. In the APOE3 stratum, ABCA7, BIN1 and PICALM passed the genome-wide significance threshold, with suggestive signals for the HLA-DQ/HLA-DR loci, CTNND2, FBN1, WLS or CSTF1 genes among others. By contrast, no genome wide significant SNPs were found in the APOE2 stratum, nor any known AD gene among suggestive signals. An additional validation of stage I+II+III findings was performed using the EADI population (stage IV), where only the APOE locus in the APOE4 stratum reached the genome wide significance threshold (Supplementary Figure 4). In the combined analysis of Stage I+II+III and Stage IV results, ABCA7, BIN1 and PICALM in the APOE3 stratum and APOE, BIN1, CLU and PICALM in the APOE4 stratum reached genome-wide significance (Supplementary Tables 4–6 and Supplementary Figure 5). Figure 1. Manhattan and QQ plots of stage I+II+III APOE2 (A, D), APOE3 (B, E) and APOE4 (C, F) stratified meta-analysis. www.aging-us.com 9280 AGING Sex stratified meta-analysis (Supplementary Tables 7, 8 and Supplementary Figure 6) identified genome-wide significant signals for BIN1 and APOE as well as suggestive signals for PICALM, MYLK, SOX5 and SCEL in the female population. By contrast in males, only suggestive signals for BIN1, APOE, ZCCHC2, the ABI3BP/IMPG2 locus, ESRRB and the 19q13.4 leukocyte receptor cluster were identified. Stratified analysis by sex and APOE (Supplementary Tables 9–14 and Supplementary Figure 7), yielded genome-wide significant signals for APOE in the APOE4 stratum in both sexes and for a 400kb 13q31.3 intergenic region containing the Ubiquitin Specific Peptidase 7 (Herpes Virus-Associated) (USP7) pseudogene (RP11-464I4.1) for APOE3 males. Among APOE4 males, we found association with AD for a large region of 1.9Mb on 3q12.1 comprising the genes CMSS1, COL8A1, FILIP1L, MIR548G, RPL24 and, in females, a 1.5Mb region on 2q33.2 comprising the ABI3 homologue ABI2, CARF, CYP20A1, FAM117B, FZD7, ICA1L, NBEAL1, RAPH1 and WDR12 genes. GWAS data: gene-level analysis Genetic marker-level results were summarized into a single measure of association for each gene. Then, association results from the combined stage I, II and III meta-analysis were used to estimate gene-wide statistics for all genes in each one of the three APOE strata (Supplementary Tables 15–17). Per stratum, genes were ranked in ascending order according to lowest to highest p values derived from the mean χ2 statistics implemented in MAGMA (Table 1). Among previously reported AD genes, APOE was the highest ranked in both APOE2 and APOE4 carriers (ranks 26 and 3 respectively), whereas BIN1 was ranked first in the APOE3 stratum (Supplementary Table 18). Known AD genes were ranked worst in the APOE2 stratum than in the others, with the complement receptor 1 (CR1), ranked in position 1292, being the second most relevant of these genes among APOE2 carriers after APOE. Differential expression analysis Blood APOE stratified DE meta-analysis between AD cases and controls (Supplementary Tables 19–21) included the ADNI and ADDN datasets. In the APOE2 stratum we identified only two upregulated (ISY1 and SRF) and two downregulated (CPT1A, PLCD1) genes below the FDR <0.05 threshold, clearly differing from expression profiles in APOE3 and APOE4 carriers (Figure 2, top 100 genes from each stratum). By contrast, APOE3 and APOE4 stratified analyses identified 1,692 and 3,293 DE genes respectively. Among genes differentially expressed in APOE4 cases versus controls we observed an over-representation of mitochondrial genes, most of them involved in the oxidative phosphorylation pathway. However, several genes from this pathway were differentially expressed in all strata but with opposite expression profiles, such as the electron transport chain genes ATP5F1, UQCRB or NDUFB3 upregulated in APOE2 cases but downregulated in APOE4 cases when compared to controls of the same haplotype. APOE3 genes were mainly cytoplasmatic genes involved in RNA metabolism. Cortex APOE-stratified DE included the MAYO, ROSMAP, MSBB, GSE15222 and GSE48350 studies. Meta-analysis of cortex datasets resulted in 518, 7714 and 1717 statistically significant genes (FDR<0.05) for the APOE2, APOE3 and APOE4 strata respectively (Supplementary Tables 22–24). As opposed to blood analyses, the overall picture is of enhanced gene expression in AD in all strata, but more pronounced in APOE2 except for XIST, strongly downregulated in AD APOE2 subjects (Figure 3, top 100 genes from each stratum). The heparan sulfate proteoglycan CD44 and the heparan sulfate lysosomal degradation enzyme IDS encoding genes were differentially expressed in all strata, with CD44 strongly upregulated in APOE2 cases and IDS downregulated in APOE4 cases. APOE2 specific genes were mostly nuclear genes involved in primary metabolic processes, as well as some apoptosis related genes (CFLAR, ATM, MCL1, AKT3 and CTSZ), all of them downregulated in AD cases but CTSZ, with higher expression in AD cases than in controls. APOE3 and APOE4 candidate genes were mainly expressed in the cytoplasm. In all strata, we identified genes involved in neuronal development (such as GFAP, BDNF or CDC42), especially in the APOE3 stratum. For both APOE2 and APOE4 strata, genes involved in vesicle mediated transport were identified, with key genes such as PCSK1, SYTL2 or SVOP downregulated in APOE4 cases. Robust rank aggregation analysis Integrative analysis was performed independently to include either blood or cortex APOE-stratified DE rankings. Thus, we combined meta-GWAS stage I-III gene-level results with blood meta-GWES results (Supplementary Tables 25–27) or with cortex meta-GWES results respectively (Supplementary Tables 28–30). In blood 275, 284 and 278 genes passed the FDR threshold in the APOE2, APOE3 and APOE4 strata respectively with 15 common genes, associated with AD irrespective of the APOE haplotype, which include APOC1, CLPTM1, DNAJA1, ING3, LARP7, NGDN, RPA3, RPL36AL, RPS24, SOD1, SRP19, and four www.aging-us.com 9281 AGING Table 1. GWAS analysis: gene level data (MAGMA results) by APOE stratum. Rank E2 HUGO NSNPS NPARAM ZSTAT PJoint PSNPwise (mean) PSNPwise (top) 1 RNF152 609 65 4.62 1.90E-06 4.59E-07 4.63E-04 2 DUOX2 150 26 3.97 3.53E-05 4.72E-06 5.12E-03 3 METRN 140 18 3.88 5.29E-05 2.41E-05 9.43E-04 4 WDR24 197 18 3.83 6.31E-05 2.71E-05 1.21E-03 5 FBXL16 196 18 3.84 6.03E-05 2.85E-05 1.11E-03 6 FAM173A 120 17 3.83 6.29E-05 2.99E-05 1.14E-03 7 JMJD8 193 18 3.79 7.59E-05 3.34E-05 1.32E-03 8 CCDC78 115 17 3.81 6.99E-05 3.68E-05 1.14E-03 9 DUOXA2 145 23 3.63 1.41E-04 3.68E-05 6.05E-03 10 HAGHL 107 15 3.83 6.43E-05 3.88E-05 9.08E-04 11 NARFL 105 15 3.83 6.51E-05 4.00E-05 8.92E-04 12 STUB1 193 18 3.72 1.01E-04 4.61E-05 1.56E-03 13 ABCB4 344 35 3.89 5.11E-05 5.27E-05 5.59E-03 14 APOC1 209 32 3.42 3.16E-04 5.40E-05 4.13E-02 15 IRGC 300 45 4.55 2.72E-06 5.52E-05 3.36E-05 Rank E3 HUGO NSNPS NPARAM ZSTAT PJoint PSNPwise (mean) PSNPwise (top) 1 BIN1 633 70 7.02 1.15E-12 8.93E-07 3.51E-12 2 FBN1 533 41 4.71 1.27E-06 4.82E-06 2.76E-04 3 WNT3 239 38 4.69 1.35E-06 6.85E-06 1.26E-04 4 CLEC4M 424 68 4.07 2.35E-05 1.35E-05 1.46E-03 5 NSF 144 22 4.76 9.48E-07 2.88E-05 3.07E-05 6 CASS4 349 50 5.00 2.80E-07 3.43E-05 4.58E-05 7 APP 998 99 3.96 3.75E-05 3.91E-05 3.43E-03 8 GPR27 306 47 3.21 6.62E-04 4.63E-05 6.29E-02 9 CRHR1 1261 23 4.07 2.39E-05 5.46E-05 2.55E-03 10 CD209 482 71 3.80 7.36E-05 6.93E-05 1.55E-03 11 SPPL2C 556 19 4.03 2.83E-05 7.77E-05 1.68E-03 12 KANSL1 978 20 4.01 2.98E-05 7.79E-05 1.44E-03 13 LRRC37A 115 6 4.01 3.09E-05 7.97E-05 4.41E-04 14 STH 460 17 3.94 4.00E-05 8.35E-05 2.88E-03 15 EIF4E3 516 83 3.01 1.29E-03 9.34E-05 1.41E-01 Rank E4 HUGO NSNPS NPARAM ZSTAT PJoint PSNPwise (mean) PSNPwise (top) 1 TOMM40 293 44 14.90 1.59E-50 1.50E-26 1.00E-50 2 APOC1 247 38 15.05 1.71E-51 3.20E-26 1.00E-50 3 APOE 270 40 14.82 5.36E-50 2.83E-24 1.00E-50 4 PVRL2 361 51 14.31 8.88E-47 1.17E-23 1.00E-50 5 APOC4 241 29 11.30 6.96E-30 1.60E-14 2.52E-33 6 APOC2 232 27 13.20 4.31E-40 8.54E-13 1.00E-50 7 CLPTM1 267 34 12.78 1.06E-37 3.43E-11 5.65E-46 8 CLU 351 46 7.60 1.47E-14 5.85E-10 4.89E-13 9 SCARA3 426 51 7.08 7.06E-13 7.15E-07 3.53E-13 10 PICALM 555 43 5.54 1.49E-08 8.39E-07 9.65E-08 11 AKAP2 601 85 4.12 1.86E-05 2.27E-06 5.58E-03 12 PALM2-AKAP2 1542 159 3.61 1.52E-04 3.72E-05 1.46E-02 13 IL6 414 48 3.72 1.01E-04 4.36E-05 7.17E-03 14 EPHX2 467 56 4.23 1.15E-05 5.26E-05 2.17E-04 15 BIN1 635 70 5.70 5.95E-09 5.72E-05 1.11E-08 www.aging-us.com 9282 AGING Figure 2. Top candidates from DE analysis in blood datasets. www.aging-us.com 9283 AGING Figure 3. Top candidates from DE analysis in cortex datasets. www.aging-us.com 9284 AGING mitochondrial proteins (ATP5F1, ATP6V1D, MRPL51 and UQCRH). The list of APOE2 specific genes is the largest one (241 genes) and include mitochondrial transporters such as SLC25A3 and SLC25A4. APOE3 specific signatures included MAPT and APP genes, along with other neuronal genes such as the cholinergic receptors CHRNA10 and CHRNA2. APOE4 specific genes included known AD genes involved in vesicle transport such as CLU and SORL1; SORL1 has been shown to regulate IL6 levels, also identified among APOE4 specific signatures. We found however a large overlap among significant genes for the APOE3 and APOE4 strata (n=51) including BIN1, MS4A4A, MS4A6A, PICALM and SLC24A4 AD genes and a good number of ribosomal and electron transport mitochondrial proteins (ATP5I, ATP5O, ATP6V1E1, COX17, MRPL27, MRPL33, MRPL35, MRPS17, MRPS21, UQCRB). APOE2 and APOE3 shared eleven genes (ACADM, AK2, ENSA, GPR132, GPR137, HSP90AA1, MCL1, NXF1, TAF1C, UBA7, ZC3H15) whereas the overlap between E2 and E4 was the smallest with eight proteins (KTN1, NDST2, POLR1D, RHOT2, STK17B, TOMM40, TSPAN32, UCHL3). At the pathway level, we observed a lower overlap between APOE strata, with only three shared mitochondrial GO categories among the three haplotypes, and little or none overlap between APOE2 and APOE3 or APOE2 and APOE4 (Supplementary Tables 31–33 and Supplementary Figure 8). In contrast, there was considerable overlap between APOE3 and APOE4 which includes mitochondria biology, secretory vesicles and antigen processing and presentation functions. In cortex, we found 376, 399 and 366 significant genes (FDR<0.05) for the APOE2, APOE3 and APOE4 strata respectively. Seven common AD candidate genes (APOC1, APOC2, CD44, CDC42, CLPTM1, DST, PGM2L1) were significant in all three strata; of them, APOC1 and CLPTM1 were also associated in the three strata in blood. In this analysis, the shortest list of APOE-specific genes was found in the APOE4 stratum, which showed the largest overlap with the APOE3 stratum; among the 45 significant genes shared by these strata, we identified several AD genes, including BIN1, MS4A4A, MS4A6A, PICALM and RIN3 in accordance with blood results. The CR1 gene is included among the 19 top genes from the APOE2 and APOE3 strata (ATPIF1, CACNB2, CDC27, CFLAR, COX15, CR1, DCLK1, GOSR2, KANSL1, KLF12, MAPT, MCL1, NSF, POGK, RUFY3, SCD5, SORBS1, SPEN, TTN), whereas APOE, TOMM40, SLC24A4 or WWOX were included among common genes for the APOE2 and APOE4 strata (21 genes: AHNAK, APOE, ARNT, CRTAP, FBXL16, GART, KALRN, KAT6A, MTMR11, OPA1, PDLIM5, PPFIA1, PURA, RBMS2, SLC24A4, SRGAP1, TOMM40, TSPAN14, UBE2F, WWOX, ZNF264). Enrichment analysis also identified both common and exclusive pathways. Common pathways for AD irrespective of the APOE haplotype were related to adhesion, neuronal development, differentiation, and lipoprotein metabolism; diverse signals related to neuronal death are also present in all three strata. Again, we observed larger overlap between APOE3 and APOE4 pathways (glial cell differentiation and activation, immunological, lipid metabolism, cardiovascular system development and heart function) than for APOE2 and APOE3 (which includes axonogenesis) or APOE2 and APOE4 (mainly phospholipid and lipoprotein metabolism due to APOE, APOC1 and APOC2 genes). (Supplementary Tables 34– 36 and Supplementary Figure 9). We observed that APOE2 exclusive pathways include chromatin regulation and telomere maintenance related processes. The APOE3 strata showed the largest number of significant enrichments, but most of them showed a similar annotation in APOE2, or, more frequently, in APOE4 strata, with the exception of antigen processing and presentation, IFNG signalling, astrocyte development and activation and myelin sheath. In APOE4 macrophage activation, fructose metabolism, vitamin D mediated inflammation, inositol phosphate metabolism and cholesterol efflux were the most relevant pathways. Clathrin vesicles, amyloid biology, inflammatory and immune response and glial cell development and differentiation appear as the most relevant categories shared by APOE3 and APOE4 strata. To identify relevant candidate blood biomarkers tracking brain changes in AD pathology we compared blood and cortex analyses (Figure 4). We identified 68 genes in common for the APOE4 stratum, including CLU, CD2AP, IL6, MS4A2, SLC25A1 or INNPP4A (Figure 4C). In APOE3, 76 common genes were found, including APP, AQP9, ATPAF, CD209, LILRA5, NDUFB3 or PTK2B (Figure 4D). Finally, in the APOE2 stratum, we identified 84 common genes including several ABC receptors (ABCA9, ABCB1, ABCB4, ABCD4), solute carrier molecules (SLC25A3, SLC25A4, SLC35E1, SLC9A9), TLR9 or IL4I1 (Figure 4B). Overlap between APOE strata-specific pathways from blood and cortex showed 6 common pathways for APOE2 (Figure 4F), half of them related to chromatin regulation, 24 common pathways for APOE3 (secretion, regulation of supramolecular fiber organization, site of polarized growth and leukocyte activation involved in inflammatory response, Figure 4G) and 18 shared pathways for APOE4, including clathrin coated vesicles, amyloid-beta processing, mitochondrial transmembrane transport, macrophage activation and monosaccharides and fructose metabolism (Figure 4H). We followed up genes with concordant profiles in both www.aging-us.com 9285 AGING Figure 4. Summary of candidate genes (A–D) and pathways (E–H) from APOE2 (B, F), APOE3 (C, G) and APOE4 (D, H) common candidates from Blood and Cortex RRA analyses. www.aging-us.com 9292 AGING Table 2. Study datasets. ApoE2 ApoE3 ApoE4 Controls Cases Controls Cases Controls Cases GWAS Stage I ADGC 191 113 810 1070 329 2063 ADDN 22 10 126 104 50 140 ADNI 40 10 161 153 67 303 GNADA 92 35 487 252 164 467 NXC 78 21 510 150 128 150 MAYO 147 24 657 233 286 478 NIA 68 9 353 94 374 297 ROSMAP 51 62 153 353 23 200 TGEN 71 25 281 261 89 420 Total Stage I 760 309 3538 2670 1510 4518 GWAS Stage II FACE 330 123 2220 1314 639 1115 GERAD 780 145 3572 1090 1574 1634 Total Stage II 1110 268 5792 2404 2213 2749 GWAS Stage III ARIC 1001 144 4479 688 1666 567 CHS 243 44 1058 240 302 133 FHS 474 27 2346 180 687 88 RS 667 102 2770 548 1001 435 Total Stage III 2385 317 10653 1656 3656 1223 GWAS Stage IV EADI 819 124 4317 993 1188 1135 Total Stage IV 819 124 4317 993 1188 1135 TOTAL GWAS 5074 1018 24300 7723 8567 9625 Blood GWES ADDN 14 11 92 79 37 107 ADNI 27 3 118 71 48 127 TOTAL Blood GWES 41 14 210 150 85 234 Cortex GWES MAYO (TCX) 27 5 112 71 43 118 ROSMAP (DLPFC) 28 26 85 147 13 92 MSBB FP 2 1 10 16 3 18 MSBB OVC 1 3 8 16 4 7 MSBB DLPF 2 0 9 12 5 16 MSBB PCG 0 3 5 14 0 14 MSBB PFC 1 1 9 15 1 17 GSE15222 26 3 114 48 37 108 GSE48350 SFG 9 1 23 8 16 10 GSE48350 EC 8 1 16 5 15 8 GSE48350 PG 10 1 21 8 13 14 TOTAL Cortex GWES 114 45 412 360 150 422 Blood Proteomics ADDN 12 10 46 76 28 110 TOTAL Blood Proteomics 12 10 46 76 28 110 Brain proteomics BANNER 6 6 29 35 6 57 BLSA 5 3 7 10 1 7 www.aging-us.com 9293 AGING MAYO 3 4 23 37 4 43 MSBB 6 10 15 88 7 44 TOTAL Cortex Proteomics 20 23 74 170 18 151 Brain snRNAseq ROSMAP 4 2 7 7 2 9 Total snRNAseq 4 2 7 7 2 9 TOTAL 5265 1112 25049 8486 8850 10551 method implemented in METAL [57] or PLINK software programs. SNPs with MAF >1% that were available in at least 60% of the datasets at each stage were included in the meta-analysis. Genomic inflation lambda (λ) was calculated using the GenABEL package [58]. Manhattans and QQ plots were generated with the qqman R package [59]. Gene-level analysis Gene level analysis was performed using MAGMA software, which compute gene-wise statistics taking into account physical distance and linkage disequilibrium (LD) between markers [60]. All SNPs with MAF above 5% were used in these analyses, setting a distance threshold of 50kb. At each stratum, genes were ranked according to the global p mean value. Genome-wide expression analysis (GWES) and meta-analysis Study cohorts Whole blood expression profiles for meta-analysis were obtained from ADNI and AddNeuroMed studies Figure 7. Integrative analysis workflow. www.aging-us.com 9294 AGING (N=734). The cortex gene-expression meta-analysis included Mount Sinai Brain Bank (MSBB) dataset (frontal pole, occipital visual cortex, dorsolateral prefrontal cortex, precentral gyrus, prefrontal cortex), ROSMAP (dorsolateral prefrontal cortex) and MAYO (temporal cortex) studies and GSE15222 [61] and GSE48350 [62] (entorhinal cortex, superior frontal cortex, post-central gyrus) datasets from the GEO repository (N=1,503). QC For these analyses, we used background corrected and normalized intensity values from expression microarrays distributed by the dataset providers, except for GSE48350. For this GEO dataset, raw. CEL files we downloaded and processed using the Robust Multi-array Average (RMA) algorithm integrated in the affy R package for background correction and normalization [63]. Diagnostic plots included Residuals vs Fitted, Residual vs Leverage, Scale Location, PCA and QQ plots; outlier values identified in these analyses were disregarded. For those datasets provided in different experimental batches, the ComBat function from the sva R package [64] was used to minimize batch effects. A multivariate regression model was fitted to adjust intensity values for covariates, including pH, postmortem interval (PMI), RNA integrity numbers (RIN), age of death, sex, race and use of lipid lowering medication when available. Differential expression analysis As for GWAS data, differential expression (DE) analysis between cases and controls was performed independently in the three APOE subgroups using R package limma [65] by dataset and brain region when available. Limma results were adjusted for multiple testing using the Benjamin and Hochberg’s (BH) method. Volcano plots and heatmaps were produced to assess these results. Probes were annotated to gene symbols using appropriate specific libraries, keeping the most differentially expressed mRNA isoform for those genes showing alternative splicing. Differential expression meta-analysis (meta-GWES) Independent APOE stratified meta-analyses were performed for combining DE results from the different datasets into single ranked gene lists for both blood and cortex. For cortex, only genes present in at least a 70% of the datasets were considered for meta-analysis. Individual logFCs were combined using the Random Effect Model (REM). Given that the analysis included data from different brain regions, genes were ranked according to the Fisher statistics to avoid making assumptions about the directionality of the effect, aimed at identifying candidate markers differentially expressed in the “majority” of studies, where Fisher methods has been described to outperform other methods in terms of power detection, biological association, stability and robustness [66]. All the analyses were performed with the metaDE R tool. Heatmap graphs were generated with the Pheatmap R package. Integrative analysis In order to obtain per-gene single estimates GWAS and GWES data were combined using the Robust Rank Aggregation (RRA) method [67]. The algorithm, integrated in the RobustRankAggreg R package, uses a probabilistic model for aggregation that is robust to noise and also facilitates the calculation of significance probabilities for all the elements in the final ranking. Two independent runs of the RRA algorithm were performed. In all of them we combined stage I+II+III GWAS meta-analysis plus blood or cortex GWES metanalyses (Figure 7). Final gene ranks for blood and cortex were generated according to ascending order of the exact p values generated by the RRA algorithm. Proteomic data analysis Proteomic data from blood (ADDN study) and brain (BANNER, BLSA, MAYO and MSBB studies) were collected. Histograms and boxplots were generated to assess the distribution of normalized intensity protein expression values distributed by data providers. Differential protein expression analyses by study and APOE stratum were performed using limma, with PMI, age, sex and, when available, lipid lowering medication as covariates. Meta-analysis of the diverse brain datasets was performed as described for GWES datasets. Single nuclei RNAseq (snRNAseq) data analysis Additionally, we explored snRNAseq cortex data from the ROSMAP study [19]. Count matrix provided by ROSMAP study was processed using Seurat package [68]. After QC (filtering out cells that have unique feature counts over 2,500 or less than 200 and cells with >5% mitochondrial counts), data were normalized and scaled. Prior to clustering the cells, we applied the Uniform Manifold Approximation and Projection (UMAP) dimensional reduction technique. Finally, a differential expression analysis between AD cases and controls was performed by each cell type using the edgeR package [69]. Enrichment analysis Enrichment analysis of RRA results was performed using four different tools: WebGestaltR [70, 71], FUMA [72] and gPROFILER [73], for genes passing www.aging-us.com 9295 AGING the multiple testing correction threshold (p=0.05), and GSEA [74] for full gene ranked lists. The databases being interrogated include GO, KEGG, WikiPathways, and Reactome. Only pathways and GO categories selected by at least two enrichment tools with adjusted p<0.05 and a minimum of three overlapping genes were selected for further exploring. Data availability statement Summary statistics are included as Supplementary Tables and will be made available through Synapse repository (https://www.synapse.org/) upon publication. Most data used in this article are publicly available (see acknowledgement section). Code availability statement Code used for this article will be made publicly available through a public Jupyter server (Madrid L, Rubio-Escudero C, Pontes B, González-Pérez A, Riquelme JA, Sáez ME. MOMIC: A Multi-Omics Pipeline for data analysis, integration and interpretation (manuscript in preparation)) AUTHOR CONTRIBUTIONS Coordination: M.E.S., A.Ruiz; Data Analyses: L.M.M, A.G.P, P.V.M.A., L.K., M.E.S.; CHARGE analysis and coordination: R.X., Q.Y., J.C.B., X.J., S.A., C.V.D., A.D.S, M.F., M.A.I., C.L.S., E.B., S.S.; EADI analysis and coordination: V.D,. C.B., B.G.-B., P.A., J.C.L.; GERAD analysis and coordination: A.Ramirez; R.S., J.W.; GR@ACE/DEGESCO analysis and coordination: S.M.G., I.R., P.G.G., A.O., A.Ruiz; Core writing group: A.C.S, L.M.M., M.E.S., A.Ruiz. All other authors contributed data and all authors critically reviewed the paper. ACKNOWLEDGMENTS Funding support for the Alzheimer’s Disease Genetics Consortium (ADGC) was provided through the NIA Division of Neuroscience (U01-AG032984). This study was downloaded from NIH dbGaP repository (phs000372.v1). The AddNeuroMed study is a public-private partnership supported by EFPIA companies and SMEs as part of InnoMed (Innovative Medicines in Europe), an Integrated Project funded by the European Union of the Sixth Framework program priority FP6-2004- LIFESCIHEALTH-5. Clinical leads responsible for data collection are Iwona Kłoszewska (Lodz), Simon Lovestone (London), Patrizia Mecocci (Perugia), Hilkka Soininen (Kuopio), Magda Tsolaki (Thessaloniki), and Bruno Vellas (Toulouse), imaging leads are Andy Simmons (London), Lars-Olad Wahlund (Stockholm) and Christian Spenger (Zurich) and bioinformatics leads are Richard Dobson (London) and Stephen Newhouse (London). This dataset was downloaded from Synapse (doi:10.7303/syn2790911). Data collection and sharing for this project was partially funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research and Development, LLC.; Johnson and Johnson Pharmaceutical Research and Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (https://www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. The Atherosclerosis Risk in Communities (ARIC) study is carried out as a collaborative study supported by the National Heart, Lung, and Blood Institute (NHLBI) contracts (HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, and HHSN268201100012C). The authors thank the staff and participants of the ARIC study for their important contributions. Funding support for “Building on GWAS for NHLBI-diseases: the U.S. CHARGE consortium” was provided by the NIH through the American Recovery and Reinvestment Act of 2009 (ARRA) (5RC2HL102419). This project was also funded from R01-NS087541. www.aging-us.com 9296 AGING The Banner Sun Health Research Institute (BANNER) data were provided by Dr. Levey from Emory University. A portion of these data were generated from samples collected through the Sun Health Research Institute Brain and Body Donation Program of Sun City, Arizona. The Brain and Body Donation Program is supported by the National Institute of Neurological Disorders and Stroke (U24 NS072026 National Brain and Tissue Resource for Parkinson’s Disease and Related Disorders), the National Institute on Aging (P30 AG19610 Arizona Alzheimer’s Disease Core Center), the Arizona Department of Health Services (contract 211002, Arizona Alzheimer’s Research Center), the Arizona Biomedical Research Commission (contracts 4001, 0011, 05-901 and 1001 to the Arizona Parkinson's Disease Consortium) and the Michael J. Fox Foundation for Parkinson's Research. This study was downloaded from Synapse (10.7303/syn7170616). The Baltimore Longitudinal Study on Aging (BLSA) study data were generated from postmortem brain tissue collected through The National Institute on Aging’s Baltimore Longitudinal Study of Aging and provided by Dr. Levey from Emory University. This study was downloaded from Synapse (10.7303/syn3606086). Infrastructure for the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium is supported in part by the National Heart, Lung, and Blood Institute grant HL105756 and for the neuroCHARGE phenotype working group through the National Institute on Aging grants R01 AG033193, RF1 AG059421, U01 AG049505 and AG052409. The Cardiovascular Health Study (CHS) research was supported by NHLBI contracts HHSN268201200036C, HHSN268200800007C, HHSN268201800001C, N01HC55222, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086, N01HC15103, HHSN268200960009C; and NHLBI grants U01HL080295, R01HL087652, R01HL105756, R01HL103612, R01HL120393, and U01HL130114 with additional contribution from the National Institute of Neurological Disorders and Stroke (NINDS). Additional support was provided through R01AG023629, and R01AG033193 from the National Institute on Aging (NIA). A full list of principal CHS investigators and institutions can be found at CHSNHLBI.org. The provision of genotyping data was supported in part by the National Center for Advancing Translational Sciences, CTSI grant UL1TR001881, and the National Institute of Diabetes and Digestive and Kidney Disease Diabetes Research Center (DRC) grant DK063491 to the Southern California Diabetes Endocrinology Research Center. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The EADI study has been developed and supported by the LABEX (laboratory of excellence program investment for the future) DISTALZ grant (Development of Innovative Strategies for a Transdisciplinary approach to ALZheimer’s disease) including funding from MEL (Metropole européenne de Lille), ERDF (European Regional Development Fund) and Conseil Régional Nord Pas de Calais. This work was supported by INSERM, the National Foundation for Alzheimer’s disease and related disorders, the Institut Pasteur de Lille and the Centre National de Recherche en Génomique Humaine, CEA, the JPND PERADES, the Laboratory of Excellence GENMED (Medical Genomics) grant no. ANR-10-LABX-0013 managed by the National Research Agency (ANR) part of the Investment for the Future program, and the FP7 AgedBrainSysBio. The Three-City Study was performed as part of collaboration between the Institut National de la Santé et de la Recherche Médicale (Inserm), the Victor Segalen Bordeaux II University and Sanofi-Synthélabo. The Fondation pour la Recherche Médicale funded the preparation and initiation of the study. The 3C Study was also funded by the Caisse Nationale Maladie des Travailleurs Salariés, Direction Générale de la Santé, MGEN, Institut de la Longévité, Agence Française de Sécurité Sanitaire des Produits de Santé, the Aquitaine and Bourgogne Regional Councils, Agence Nationale de la Recherche, ANR supported the COGINUT and COVADIS projects. Fondation de France and the joint French Ministry of Research/INSERM “Cohortes et collections de données biologiques” programme. Lille Génopôle received an unconditional grant from Eisai. The Three-city biological bank was developed and maintained by the laboratory for genomic analysis LAG-BRC - Institut Pasteur de Lille. This work was further supported by the CoSTREAM project (http://www.costream.eu/) and funding from the European Union's Horizon 2020 research and innovation program under grant agreement 667375. Pascual Sanchez Juan was supported by grants from, IDIVAL, Instituto de Salud Carlos III (Fondo de Investigacion Sanitario, PI08/0139, PI12/02288, PI16/01652, JPND (DEMTEST PI11/03028) and the CIBERNED program. Belgium samples: Research at the Antwerp site is funded in part by the Belgian Science Policy Office Interuniversity Attraction Poles program, the Belgian Alzheimer Research Foundation, the Flemish government-initiated Flanders Impulse Program on Networks for Dementia Research (VIND) and the Methusalem excellence program, the Research Foundation Flanders (FWO), and the University of Antwerp Research Fund, Belgium. The Antwerp site authors thank the personnel of the VIB Neuromics www.aging-us.com 9297 AGING Support Facility, the Biobank of the Institute Born- Bunge and neurology departments at the contributing hospitals. The authors acknowledge the members of the BELNEU consortium for their contributions to the clinical and pathological characterization of Belgium patients and the personnel of the Diagnostic Service Facility for the genetic testing. Finish sample collection: Financial support for this project was provided by Academy of Finland (grant number 307866), Sigrid Jusélius Foundation and the Strategic Neuroscience Funding of the University of Eastern Finland Swedish sample collection: Financially supported in part by the Swedish Brain Power network, the Marianne and Marcus Wallenberg Foundation, the Swedish Research Council (521-2010- 3134, 2015-02926), the King Gustaf V and Queen Victoria’s Foundation of Freemasons, the Regional Agreement on Medical Training and Clinical Research (ALF) between Stockholm County Council and the Karolinska Institutet, the Swedish Brain Foundation and the Swedish Alzheimer Foundation”. This work has been partly supported by the National Heart, Lung and Blood Institute's Framingham Heart Study (Contract No. N01-HC-25195 and No. HHSN268201500001I) and its contract with Affymetrix, Inc. for genotyping services (Contract No. N02-HL-6-4278). A portion of this research utilized the Linux Cluster for Genetic Analysis (LinGA-II) funded by the Robert Dawson Evans Endowment of the Department of Medicine at Boston University School of Medicine and Boston Medical Center. This study was also supported by grants from the National Institute of Aging (R01s AG033040, AG033193, AG054076, AG049607, AG008122, AG016495; and U01- AG049505) and the National Institute of Neurological Disorders and Stroke (R01-NS017950). We would like to thank the dedication of the Framingham Study participants, as well as the Framingham Study team, especially investigators and staff from the Neurology group, for their contributions to data collection. Dr. DeCarli is supported by the Alzheimer’s Disease Center (P30 AG 010129). The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute; the National Institutes of Health; or the U.S. Department of Health and Human Services. The genotypic and associated phenotypic data used in the study, Multi-Site Collaborative Study for Genotype- Phenotype Associations in Alzheimer’s Disease (GenADA) study was contributed by Luis Fornazzari. Memory Clinic, St. Michaels Hospital, Toronto, Serge Gauthier. McGill Centre for Studies in Aging, Alzheimer's Disease Research Unit, Verdun, Quebec, Peter H. St. George-Hyslop. University of Toronto, Center for Research in Neurodegenerative Diseases, Toronto, Ontario, Howard Feldman. Vancouver Hospital and Health Sciences Centre, Vancouver, British Columbia, Anthony Guzman. Clinical Trial Unit, SCO Health Service, Ottawa, Ontario, Michael Borrie. Parkwood Hospital, London, Ontario, Andrew Kertesz. St. Joseph's Hospital, London, Ontario, Richard Delisle. Clinique de Neurologie, Trois- Riviéres, Quebec. The study was funded by GlaxoSmithKline, Inc.. Translational Medicine and Genetics, Clinical Imaging Center, R&D Alliances, and Worldwide Epidemiology. The dataset used for analyses described in this manuscript were obtained from NIH dbGaP repository (phs000219.v1.p1). Data used in the preparation of this article was obtained from the Genetic and Environmental Risk for Alzheimer’s disease (GERAD1) Consortium (Harold et al. 2009). Cardiff University was supported by the Wellcome Trust, Medical Research Council (MRC), Alzheimer’s Research UK (ARUK) and the Welsh Assembly Government. Cambridge University and Kings College London acknowledge support from the MRC. ARUK supported sample collections at the South West Dementia Bank and the Universities of Nottingham, Manchester and Belfast. The Belfast group acknowledges support from the Alzheimer's Society, Ulster Garden Villages, N.Ireland R&D Office and the Royal College of Physicians/Dunhill Medical Trust. The MRC and Mercer’s Institute for Research on Ageing supported the Trinity College group. The South West Dementia Brain Bank acknowledges support from Bristol Research into Alzheimer's and Care of the Elderly. The Charles Wolfson Charitable Trust supported the OPTIMA group. Washington University was funded by NIH grants, Barnes Jewish Foundation and the Charles and Joanne Knight Alzheimer's Research Initiative. Patient recruitment for the MRC Prion Unit/UCL Department of Neurodegenerative Disease collection was supported by the UCLH/UCL Biomedical Centre and NIHR Queen Square Dementia Biomedical Research Unit. LASER-AD was funded by Lundbeck SA. The Bonn group was supported by the German Federal Ministry of Education and Research (BMBF), Competence Network Dementia and Competence Network Degenerative Dementia, and by the Alfried Krupp von Bohlen und Halbach-Stiftung. The GERAD1 Consortium also used samples ascertained by the NIMH AD Genetics Initiative. The Genome Research @ Fundació ACE project (GR@ACE) is supported by Grifols SA, Fundación bancaria ‘La Caixa’, Fundació ACE, and CIBERNED. A.R. receive support from the European Union/EFPIA Innovative Medicines Initiative Joint undertaking www.aging-us.com 9298 AGING ADAPTED and MOPEAD projects (grant numbers 115975 and 115985, respectively) and by national grants PI19/01301, PI16/01861, PI17/01474 and PI19/01240. Acción Estratégica en Salud is integrated into the Spanish National R + D + I Plan and funded by ISCIII (Instituto de Salud Carlos III)–Subdirección General de Evaluación and the Fondo Europeo de Desarrollo Regional (FEDER–‘Una manera de hacer Europa’). Some control samples and data from patients included in this study were provided in part by the National DNA Bank Carlos III (http://www.bancoadn.org/, University of Salamanca, Spain) and Hospital Universitario Virgen de Valme (Sevilla, Spain); they were processed following standard operating procedures with the appropriate approval of the Ethical and Scientific Committee. The Mayo Clinic Alzheimer's Disease Genetic Studies, led by Dr. Nilüfer Ertekin-Taner and Dr. Steven G. Younkin, Mayo Clinic, Jacksonville, FL using samples from the Mayo Clinic Study of Aging, the Mayo Clinic Alzheimer's Disease Research Center, and the Mayo Clinic Brain Bank. Data collection was supported through funding by NIA grants P50 AG016574, R01 AG032990, U01 AG046139, R01 AG018023, U01 AG006576, U01 AG006786, R01 AG025711, R01 AG017216, R01 AG003949, NINDS grant R01 NS080820, CurePSP Foundation, and support from Mayo Foundation. This dataset was downloaded from Synapse (doi:10.7303/syn5550404). The Mount Sinai Brain Bank (MSBB) study data were provided by Dr. Levey from Emory University based on postmortem brain tissue collected through the Mount Sinai VA Medical Center Brain Bank provided by Dr. Eric Schadt from Mount Sinai School of Medicine. This dataset was downloaded from Synapse (10.7303/syn3159438). The Neocodex-Murcia study was funded by the Fundación Alzheimur (Murcia), the Ministerio de Educación y Ciencia (Gobierno de España), Corporación Tecnológica de Andalucía and Agencia IDEA (Consejería de Innovación, Junta de Andalucía). The Diabetes Research Laboratory, Biomedical Research Foundation. University Hospital Clínico San Carlos has been supported by CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM); CIBERDEM is an ISCIII Project. The National Institute on Aging (NIA) - Late Onset Alzheimer's Disease Family Study data were provided by Richard Mayeux, MD, MSc. Columbia University, New York, NY, USA andTatiana Foroud, PhD. National Cell Repository for Alzheimer's Disease and Indiana University, Indianapolis, IN, USA. This study has received funding from HHSN268200782096C. "NIH contract High throughput genotyping for studying the genetic contributions to human disease". National Institutes of Health, Bethesda, MD, USA. The study was downloaded from dbGap (phs000168p2v2). The ROS/MAP study data were provided by the Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago. Data collection was supported through funding by NIA grants P30AG10161 (ROS), R01AG15819 (ROSMAP; genomics and RNAseq), R01AG17917 (MAP), R01AG30146, R01AG36042 (5hC methylation, ATACseq), RC2AG036547 (H3K9Ac), R01AG36836 (RNAseq), R01AG48015 (monocyte RNAseq) RF1AG57473 (single nucleus RNAseq), U01AG32984 (genomic and whole exome sequencing), U01AG46152 (ROSMAP AMP-AD, targeted proteomics), U01AG46161(TMT proteomics), U01AG61356 (whole genome sequencing, targeted proteomics, ROSMAP AMP-AD), the Illinois Department of Public Health (ROSMAP), and the Translational Genomics Research Institute (genomic). Additional phenotypic data can be requested at https://www.radc.rush.edu/. This dataset was downloaded from Synapse (doi:10.7303/syn3219045). The Rotterdam Study (RS) is funded by Erasmus Medical Center and Erasmus University, Rotterdam, Netherlands Organization for the Health Research and Development (ZonMw), the Research Institute for Diseases in the Elderly (RIDE), the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the Municipality of Rotterdam. The authors are grateful to the study participants, the staff from the Rotterdam Study and the participating general practitioners and pharmacists. The generation and management of GWAS genotype data for the Rotterdam Study (RS I, RS II, RS III) were executed by the Human Genotyping Facility of the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands. The GWAS datasets are supported by the Netherlands Organisation of Scientific Research NWO Investments (nr. 175.010.2005.011, 911-03-012), the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, the Research Institute for Diseases in the Elderly (014-93-015; RIDE2), the Netherlands Genomics Initiative (NGI)/Netherlands Organisation for Scientific Research (NWO) Netherlands Consortium for Healthy Aging (NCHA), project nr. 050-060-810. We thank Pascal Arp, Mila Jhamai, Marijn Verkerk, Lizbeth Herrera and Marjolein Peters, and Carolina Medina-Gomez, for their help in creating the GWAS database, and Karol Estrada, www.aging-us.com 9299 AGING Yurii Aulchenko, and Carolina Medina-Gomez, for the creation and analysis of imputed data. This work has been performed as part of the CoSTREAM project (http://www.costream.eu/) and ORACLE project, and has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 667375 and No 678543. HHHA was supported by ZonMW grant number 916.19.151. The TGEN study was supported by Kronos Life Sciences Laboratories, the National Institute on Aging (Arizona Alzheimer’s Disease Center P30 AG19610, RO1 AG023193, Mayo Clinic Alzheimer’s Disease Center P50 AG16574, and Intramural Research Program), the National Alzheimer’s Coordinating Center (U01 AG016976), and the state of Arizona. TGEN investigators provided free access to genotype data to other researchers via Coriell Biorepositories (http://www.coriell.org). We thank Dr Beatriz Pontes, Dr Cristina Rubio- Escudero and Dr José C. Riquelme for providing support for the implementation and distribution of analysis code as Jupyter notebooks. 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Wijsman EM, Pankratz ND, Choi Y, Rothstein JH, Faber KM, Cheng R, Lee JH, Bird TD, Bennett DA, Diaz-Arrastia R, Goate AM, Farlow M, Ghetti B, et al, and NIA-LOAD/NCRAD Family Study Group. Genome-wide association of familial late-onset Alzheimer’s disease replicates BIN1 and CLU and nominates CUGBP2 in interaction with APOE. PLoS Genet. 2011; 7:e1001308. https://doi.org/10.1371/journal.pgen.1001308 PMID:21379329 19. Mathys H, Davila-Velderrain J, Peng Z, Gao F, Mohammadi S, Young JZ, Menon M, He L, Abdurrob F, Jiang X, Martorell AJ, Ransohoff RM, Hafler BP, et al. Author Correction: Single-cell transcriptomic analysis of Alzheimer's disease. Nature. 2019; 571:E1. https://doi.org/10.1038/s41586-019-1329-6 PMID:31209304 20. Hatcher C, Relton CL, Gaunt TR, Richardson TG. Leveraging brain cortex-derived molecular data to elucidate epigenetic and transcriptomic drivers of complex traits and disease. Transl Psychiatry. 2019; 9:105. https://doi.org/10.1038/s41398-019-0437-2 PMID:30820025 21. Raj T, Li YI, Wong G, Humphrey J, Wang M, Ramdhani S, Wang YC, Ng B, Gupta I, Haroutunian V, Schadt EE, Young-Pearse T, Mostafavi S, et al. Integrative transcriptome analyses of the aging brain implicate altered splicing in Alzheimer’s disease susceptibility. Nat Genet. 2018; 50:1584–92. https://doi.org/10.1038/s41588-018-0238-1 PMID:30297968 22. Traylor M, Adib-Samii P, Harold D, Dichgans M, www.aging-us.com 9301 AGING Williams J, Lewis CM, Markus HS, and Alzheimer's Disease Neuroimaging Initiative, and International Stroke Genetics Consortium (ISGC), and UK Young Lacunar Stroke DNA resource, and METASTROKE, and International Genomics of Alzheimer's Project (IGAP), and investigators. Shared genetic contribution to Ischaemic Stroke and Alzheimer's Disease. Ann Neurol. 2016; 79:739–47. https://doi.org/10.1002/ana.24621 PMID:26913989 23. Bihlmeyer NA, Merrill E, Lambert Y, Srivastava GP, Clark TW, Hyman BT, Das S. Novel methods for integration and visualization of genomics and genetics data in Alzheimer’s disease. Alzheimers Dement. 2019; 15:788–98. https://doi.org/10.1016/j.jalz.2019.01.011 PMID:30935898 24. Rao S, Ghani M, Guo Z, Deming Y, Wang K, Sims R, Mao C, Yao Y, Cruchaga C, Stephan DA, Rogaeva E. An APOE- independent cis-eSNP on chromosome 19q13.32 influences tau levels and late-onset Alzheimer’s disease risk. Neurobiol Aging. 2018; 66:178.e1–8. https://doi.org/10.1016/j.neurobiolaging.2017.12.027 PMID:29395286 25. Jiang S, Zhang CY, Tang L, Zhao LX, Chen HZ, Qiu Y. Integrated Genomic Analysis Revealed Associated Genes for Alzheimer’s Disease in APOE4 Non-Carriers. Curr Alzheimer Res. 2019; 16:753–63. https://doi.org/10.2174/156720501666619082312472 4 PMID:31441725 26. Yamaguchi-Kabata Y, Morihara T, Ohara T, Ninomiya T, Takahashi A, Akatsu H, Hashizume Y, Hayashi N, Shigemizu D, Boroevich KA, Ikeda M, Kubo M, Takeda M, Tsunoda T. 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Arch Neurol. 2002; 59:972–76. https://doi.org/10.1001/archneur.59.6.972 PMID:12056933 33. de la Monte SM, Luong T, Neely TR, Robinson D, Wands JR. Mitochondrial DNA damage as a mechanism of cell loss in Alzheimer’s disease. Lab Invest. 2000; 80:1323–35. https://doi.org/10.1038/labinvest.3780140 PMID:10950123 34. Song XN, Zhang LQ, Liu DG, Lin J, Zheng JD, Dai DP, Hei AL, Hayakawa H, Sekiguchi M, Cai JP. Oxidative damage to RNA and expression patterns of MTH1 in the hippocampi of senescence-accelerated SAMP8 mice and Alzheimer’s disease patients. Neurochem Res. 2011; 36:1558–65. https://doi.org/10.1007/s11064-011-0484-4 PMID:21538080 35. Zhou X, Chen Y, Mok KY, Kwok TC, Mok VC, Guo Q, Ip FC, Chen Y, Mullapudi N, Giusti-Rodríguez P, Sullivan PF, Hardy J, Fu AK, et al, and Alzheimer’s Disease Neuroimaging Initiative. Non-coding variability at the APOE locus contributes to the Alzheimer’s risk. Nat Commun. 2019; 10:3310. https://doi.org/10.1038/s41467-019-10945-z PMID:31346172 36. Rothaug M, Becker-Pauly C, Rose-John S. The role of interleukin-6 signaling in nervous tissue. Biochim Biophys Acta. 2016; 1863:1218–27. https://doi.org/10.1016/j.bbamcr.2016.03.018 PMID:27016501 www.aging-us.com 9308 AGING The mayo clinic LOAD genome-wide association study (MAYO) Subjects from the Mayo LOAD GWAS were selected from two clinical AD Case-Control series: Mayo Clinic Jacksonville (MCJ), Mayo Clinic Rochester (MCR)and a neuropathological series of autopsy-confirmed subjects from the Mayo Clinic Brain Bank [15]. All subjects from the clinical series (MCJ and MCR) were diagnosed by a Mayo Clinic neurologist; all control subjects had a Clinical Dementia Rating score of zero at the most recent time of testing; all LOAD patients had a diagnosis of probable or possible AD according to the NINCDS-ADRDA criteria [16]. All ADs had definite diagnosis according to the NINCDS-ADRDA criteria and had Braak scores of ≥4.0. All non–AD Controls had Braak scores of ≤2.5; many had brain pathology unrelated to AD. The Mount Sinai Brain Bank (MSBB) study Brain specimens were obtained from the Mount Sinai/JJ Peters VA Medical Center Brain Bank (MSBB) which holds over 1,700 samples. This cohort was assembled after applying stringent inclusion/exclusion criteria and represents the full spectrum of disease severity. Neuropathological assessments are performed according to the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) protocol and include assessment by hematoxylin and eosin, modified Bielschowski, modified thioflavin S, and anti-β amyloid (4G8), anti-tau (AD2) and anti-ubiquitin (Daka Corp.). Each case is assigned a Braak AD-staging score for progression of neurofibrillary neuropathology. Quantitative data regarding the density of neuritic plaques in the middle frontal gyrus, orbital frontal cortex, superior temporal gyrus, inferior parietal cortex and calcarine cortex are also collected as described. Clinical dementia rating scale (CDR) and mini–mental state examination (MMSE) severity tests are conducted for assessment of dementia and cognitive status. Final diagnoses and CDR scores are conferred by consensus. Based on CDR classification, subjects are grouped as no cognitive deficits (CDR = 0), questionable dementia (CDR = 0.5), mild dementia (CDR = 1.0), moderate dementia (CDR = 2.0), and severe to terminal dementia (CDR = 3.0–5.0). Covariates including demographic and neuropathological data were collected on the samples used for this project including postmortem interval, race, age of death, clinical dementia rating, clinical neuropathology diagnosis, CERAD, Braak, sex, and a series of neuropathological variables. The Neocodex-Murcia study (NXC) The study includes 327 sporadic AD patients and 801 controls with unknown cognitive status from the Spanish general population collected by Neocodex [17, 18]. AD patients were diagnosed as possible or probable AD in accordance with the criteria of the National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer's Disease and Related Disorders Association (NINCDS-ADRDA) [16]. The national institute on aging - late onset Alzheimer's disease family study (NIA) The goal of this study is to identify and recruit families with two or more siblings with the late-onset form of Alzheimer's disease and a cohort of unrelated, nondemented controls similar in age and ethnic background, and to make the samples, the clinical and genotyping data and preliminary analyses available to qualified investigators world-wide [19]. Genotyping by the Center for Inherited Disease Research (CIDR) was performed using the Illumina Infinium II assay protocol with hybridization to Illumina Human 610Quadv1_B Beadchips. The religious orders study and memory and aging project (ROS/MAP) study The Religious Orders Study (ROS) is a longitudinal clinical-pathologic cohort study of aging and Alzheimer's disease (AD) from the Rush University that enrolled individuals from religious communities for longitudinal clinical analysis and brain donation [20]. Participants were enrolled from more than 40 groups of religious orders (nuns, priests, brothers) across the United States. Medical conditions are documented starting in 1994 by clinical evaluation or self-report. Alzheimer's Disease status was determined by a computer algorithm based on cognitive test performance with a series of discrete clinical judgments made in series by a neuropsychologist and a clinician. The Memory and Aging Project (MAP) is a longitudinal, epidemiologic clinical-pathologic cohort study of common chronic conditions of aging with an emphasis on decline in cognitive and motor function and risk of Alzheimer’s disease that began in 1997 and is run from Rush University [20]. This study was designed to complement the ROS study by enrolling individuals with a wider range of life experiences and socioeconomic status into a study of similar structure and design as ROS. The study enrolled older individuals without any signs of dementia, primarily recruiting from continuous care retirement communities throughout north-eastern Illinois, USA. Diagnoses of dementia and AD are performed in an identical manner to the ROS study. www.aging-us.com 9309 AGING The Rotterdam study The Rotterdam Study is a prospective, population-based cohort study among individuals living in the welldefined Ommoord district in the city of Rotterdam in The Netherlands [21, 22]. The aim of the study is to determine the occurrence of cardiovascular, neurological, ophthalmic, endocrine, hepatic, respiratory, and psychiatric diseases in elderly people. The cohort was initially defined in 1990 among approximately 7,900 persons, aged 55 years and older, who underwent a home interview and extensive physical examination at the baseline and during followup rounds every 3-4 years (RS-I). The cohort was extended in 2000/2001 (RS-II, 3,011 individuals aged 55 years and older) and 2006/2008 (RS-III, 3,932 subjects, aged 45 and older). Written informed consent was obtained from all participants and the Medical Ethics Committee of the Erasmus Medical Center, Rotterdam, approved the study. The Translational Genomics Research Institute (TGEN) study The TGEN GWAS study included 643 late onset AD cases and 404 controls from a neuropathological cohort, and 197 late onset AD cases and 114 controls from a clinical cohort [23]. CONSORTIA MEMBERSHIP ADAPTED consortium Margot Bakker1, Lamiaa Bahnassawy1, Peter Reinhardt1, Janina Ried1, Eric Mohler1, Heike Hering2, Maria Eugenia Sáez3, Antonio González-Pérez3, Laura Madrid3,Santos Mañes4, Keiryn Bennett5, Cornelia van Duijn6, Shahzad Ahmad6, Agustin Ruiz7, Adela Orellana7, Pablo García7, Itziar de Rojas7, Carlos Vicario8, Eva Díaz-Guerra8, Esther Arribas-González8, Alfredo Cabrera9, Thomas Hankemeier10, Isabelle Kohker10, Remko van Vught11, Tania Fowke11, Bridget Glaysher12, Michael Peitz13, Alfredo Ramírez13, Pamela Martino Adami13, Carolina Dalmasso13, Luca Kleineidam13. 1 AbbVie Deutschland GmbH & Co. KG, Genomics Research Center, Knollstrasse, 67061 Ludwigshafen, Germany; 2 Biogen Inc., Cambridge, MA, USA; 3 Andalusion Bioiformatics Research Centre (CAEBi)Sevilla, Spain; 4 Department of Immunology and Oncology, Centro Nacional de Biotecnología (CNB/CSIC); 5 DC Biosciences, James Lindsay Place, Dundee, DD1 5JJ, UK; 6 Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands; 7 Research Center and Memory clinic Fundació ACE, Institut Català de Neurociències Aplicades, Universitat Internacional de Catalunya, Barcelona, Spain; 8 Instituto Cajal , CSIC , Madrid, Spain; 9 . Janssen Research and Development, a Division of Janssen Pharmaceutica N.V., Beerse, Belgium; 10 Analytical Biosciences, Leiden Academic Centre for Drug Research, Leiden University, Leiden, Netherlands; 11 MIMETAS BV, JH Oortweg 19, Leiden, CH, 2333 The Netherlands; 12 MODUS Research and Innovation Ltd., Unit D Tayside Software Centre, Gemini Crescent, Dundee Technology Park, Dundee, DD2 1TY, UK; 13 Division of Neurogenetics and Molecular Psychiatry, Department of Psychiatry and Psychotherapy, Medical Faculty, University of Cologne, Cologne, Germany. Cohorts for heart and aging research in genomic epidemiology consortium (CHARGE) consortium Maria Vronskaya, Aura Frizatti, Nandini Badarinarayan, Rachel Raybould, Taniesha Morgan, Per Hoffmann, Denise Harold, Amy Gerrish, Nicola Denning, Nick C. Fox, Joseph T. Hughes, Yogen Patel, Makrina Daniilidou, James Uphill, Daniela Galimberti, Elio Scarpini, Johannes Kornhuber, Sabrina Sordon, Manuel Mayhaus, Wei Gu, Annette M Hartmann, Simon Lovestone, Rebecca Sussams, Clive Holmes, Wolfgang Maier, Amit Kawalia, Susanne Moebus, James Turton, Jenny Lord, Iwona Kloszewska, Aoibhinn Lynch, Brian Lawlor, Michael Gill, Monica Diez-Fairen, Ignacio Alvarez, Antonio Ciaramella, Chiara Cupidi, Raffaele Giovanni Maletta, Roberta Cecchetti, Magda Tsolaki, David Craig, Despoina Avramidou, Antonia Germanou, Maria Koutroumani, Olymbia Gkatzima, Harald Hampel, David C. Rubinsztein, Lutz Frölich, Bernadette McGuinness, Janet A. Johnston, Peter Passmore, Jonathan M. Schott, Jason D. Warren, Michelle K. Lupton, Petra Proitsi, John Powell, John S. K. Kauwe, Michelangelo Mancuso, Ubaldo Bonuccelli, Andrew McQuillin, Gill Livingston, Nicholas J. Bass, John Hardy, Jose Bras, Minerva M. Carrasquillo, Rita Guerreiro, Elizabeth Fisher, Carlo Masullo, Gina Bisceglio, Li Ma, Neill R. Graff-Radford, Angela Hodges, Martin Scherer, Matthias Riemenschneider, Reinhard Heun, Heike Kölsch, Markus Leber, Isabella Heuser, Ina Giegling, Michael Hüll, John Morris, Kevin Mayo, Thomas Feulner, Dmitriy Drichel, Thomas D. Cushion, Paul Hollingworth, Rachel Marshall, Alun Meggy, Georgina Menzies, Ganna Leonenko, Detelina Grozeva, Giancarlo Russo, Frank Jessen, Bruno Vellas, Emma Vardy, Karl-Heinz Jöckel, Martin Dichgans, David Mann, Stuart Pickering-Brown, Norman Klopp, H-Erich Wichmann, Kevin Morgan, Kristelle Brown, Christopher Medway, Markus M. Nöthen, Nigel M. Hooper, Antonio Daniele, Anthony Bayer, John Gallacher, Hendrik van den Bussche, Carol Brayne, www.aging-us.com 9310 AGING Steffi Riedel-Heller, Ammar Al-Chalabi, Christopher E. Shaw, Jens Wiltfang, Victoria Alvarez, Andrew B. Singleton, John Collinge, Simon Mead, Martin Rossor, Natalie S. Ryan, Benedetta Nacmias, Sandro Sorbi, Eleonora Sacchinelli, Gianfranco Spalletta, Carlo Caltagirone, Maria Donata Orfei, Robert Clarke, A. David Smith, Donald Warden, Gordon Wilcock, Amalia Cecilia Bruni, Maura Gallo, Yoav Ben-Shlomo, Patrizia Mecocci, Pau Pastor, Oliver Peters, Virginia Boccardi, Nick Warner, Panagiotis Deloukas, Carlos Cruchaga, Rhian Gwilliam, Patrick G. Kehoe, Seth Love, Chris Corcoran, JoAnn Tschanz, Ron Munger, Michael C. O’Donovan, Lesley Jones, Michael J. Owen, Valentina Escott-Price, Alfredo Ramirez, Peter A. Holmans, Julie Williams. The European Alzheimer's disease initiative (EADI) consortium Benjamin Grenier-Boley, Vincent Damotte, Anne Boland, Céline Bellenguez, Kristel Sleegers, Robert Olaso, Mikko Hiltunen, Jacques Epelbaum, Jean- Guillaume Garnier, Marie-Laure Moutet, Delphine Bacq, Maria Del Zompo, Ignacio Mateo, Florentino Sanchez-Garcia, Maria Candida Deniz Naranjo, David Wallon, Fabienne Garzia, Bertrand Fin, Stéphane Meslage, Sebastiaan Engelborghs, Rik Vandenberghe, Peter De Deyn, Alessio Squassina, Eloy Rodriguez- Rodriguez, Carmen Munoz-Fernadez, Yolanda Aladro Benito, Hakan Thonberg, Vilmantas Giedraitis, Lena Kilander, RoseMarie Brundin, Letizia Concari, Seppo Helisalmi, Anne Maria Koivisto, Annakaisa Haapasalo, Vincenzo Solfrizzi, Vincenza Frisardi, Vincent Deramecourt, Francesca Salani, Nathalie Fievet, Olivier Hanon, Carole Dufouil, Alexis Brice, Karen Ritchie, Bruno Dubois, Hilkka Soininen, Laura Fratiglioni, Lina Keller, Francesco Panza, Didier Hannequin, Paolo Caffarra, Lars Lannfelt, Florence Pasquier, Paola Bossù, Alberto Pilotto, Maria J. Bullido, Paola Bosco, Eliecer Coto, Alberto Lleo, Martin Ingelsson, Caroline Graff, Pascual Sanchez-Juan, Claudine Berr, Stéphanie Debette, Jean-Francois Dartigues, Gael Nicolas, Dominique Campion, Jordi Clarimon, Christine Van Broeckhoven, Jean-François Deleuze, Phillippe Amouyel, Jean-Charles Lambert. The GERAD study GERAD1 Collaborators: Denise Harold1, Rebecca Sims1, Amy Gerrish1, Jade Chapman1, Valentina Escott- Price1, Nandini Badarinarayan1, Richard Abraham1, Paul Hollingworth1, Marian Hamshere1, Jaspreet Singh Pahwa1, Kimberley Dowzell1, Amy Williams1, Nicola Jones1, Charlene Thomas1, Alexandra Stretton1, Angharad Morgan1, Kate Williams1, Sarah Taylor1, Simon Lovestone2, John Powell3, Petroula Proitsi3, Michelle K Lupton3, Carol Brayne4, David C. Rubinsztein5, Michael Gill6, Brian Lawlor6, Aoibhinn Lynch6, Kevin Morgan7, Kristelle Brown7, Peter Passmore8, David Craig8, Bernadette McGuinness8, Janet A Johnston8, Stephen Todd8, Clive Holmes9, David Mann10, A. David Smith11, Seth Love12, Patrick G. Kehoe12, John Hardy13, Rita Guerreiro14,15, Andrew Singleton14, Simon Mead16, Nick Fox17, Martin Rossor17, John Collinge16, Wolfgang Maier18, Frank Jessen18, Reiner Heun18, Britta Schürmann18,19, Alfredo Ramirez18, Tim Becker20, Christine Herold20, André Lacour20, Dmitriy Drichel20, Hendrik van den Bussche21, Isabella Heuser22, Johannes Kornhuber23, Jens Wiltfang24, Martin Dichgans25,26, Lutz Frölich27, Harald Hampel28, Michael Hüll29, Dan Rujescu30, Alison Goate31, John S.K. Kauwe32, Carlos Cruchaga33, Petra Nowotny33, John C. Morris33, Kevin Mayo33, Gill Livingston34, Nicholas J. Bass34, Hugh Gurling34, Andrew McQuillin34, Rhian Gwilliam35, Panagiotis Deloukas35, Markus M. Nöthen20, Peter Holmans1, Michael O’Donovan1, Michael J.Owen1, Julie Williams1. 1Medical Research Council (MRC) Centre for Neuropsychiatric Genetics and Genomics, Neurosciences and Mental Health Research Institute, Department of Psychological Medicine and Neurology, School of Medicine, Cardiff University, Cardiff, UK. 2Department of Psychiatry, Medical Sciences Division, University of Oxford, Oxford, UK. 3Kings College London, Institute of Psychiatry, Department of Neuroscience, De Crespigny Park, Denmark Hill, London, UK. 4Institute of Public Health, University of Cambridge, Cambridge, UK. 5Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK. 6Mercers Institute for Research on Aging, St. James Hospital and Trinity College, Dublin, Ireland. 7Institute of Genetics, Queens Medical Centre, University of Nottingham, UK. 8Ageing Group, Centre for Public Health, School of Medicine, Dentistry and Biomedical Sciences, Queens University Belfast, UK. 9Division of Clinical Neurosciences, School of Medicine, University of Southampton, Southampton, UK. 10Clinical Neuroscience Research Group, Greater Manchester Neurosciences Centre, University of Manchester, Salford, UK. 11Oxford Project to Investigate Memory and Ageing (OPTIMA), University of Oxford, Department of Pharmacology, Mansfield Road, Oxford, UK. 12University of Bristol Institute of Clinical Neurosciences, School of Clinical Sciences, Frenchay Hospital, Bristol, UK. 13Department of Molecular Neuroscience and Reta Lilla Weston Laboratories, Institute of Neurology, UCL, London, UK. 14Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, United States of America. 15Department of www.aging-us.com 9311 AGING Molecular Neuroscience, Institute of Neurology, University College London, Queen Square, London WC1N 3BG, UK. 16MRC Prion Unit, Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK. 17Dementia Research Centre, Department of Neurodegenerative Diseases, University College London, Institute of Neurology, London, UK. 18Department of Psychiatry, University of Bonn, Sigmund-Freud-Straβe 25, 53105 Bonn, Germany. 19Institute for Molecular Psychiatry, University of Bonn, Bonn, Germany. 20Department of Genomics, Life and Brain Center, University of Bonn, Bonn, Germany. 21Institute of Primary Medical Care, University Medical Center Hamburg-Eppendorf, Germany. 22Department of Psychiatry, Charité Berlin, Germany. 23Department of Psychiatry, Friedrich-Alexander-University Erlangen- Nürnberg, Germany. 24Department of Psychiatry and Psychotherapy, University Medical Center (UMG), Georg-August-University, Göttingen, Germany. 25Institute for Stroke and Dementia Research, Klinikum der Universität München, Marchioninistr. 15, 81377, Munich, Germany. 26Department of Neurology, Klinikum der Universität München, Marchioninistr. 15, 81377, Munich, Germany. 27Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Germany. 28Institute for Memory and Alzheimer’s Disease and INSERM, Sorbonne Universities, Pierre and Marie Curie University, Paris, France; Institute for Brain and Spinal Cord Disorders (ICM), Department of Neurology, Hospital of Pitié- Salpétrière, Paris, France. 29Centre for Geriatric Medicine and Section of Gerontopsychiatry and Neuropsychology, Medical School, University of Freiburg, Germany. 30Department of Psychiatry, University of Halle, Halle, Germany. 31Neuroscience Department, Icahn School of Medicine at Mount Sinai, New York, US. 32Department of Biology, Brigham Young University, Provo, UT, 84602, USA. 33Departments of Psychiatry, Neurology and Genetics, Washington University School of Medicine, St Louis, MO 63110, US. 34Department of Mental Health Sciences, University College London, UK. 35The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK. The GR@ACE/DEGESCO consortium The GR@ACE study group: Abdelnour C1,2, Aguilera N1, Alarcon E1,3, Alegret M1,2, Benaque A1, Boada M1,2, Buendia M1, Cañabate P1,2, Carracedo A4,5, Corbatón A6, de Rojas I1, Diego S1, Espinosa A1,2, Gailhajenet A1, García González P1, Gil S1, Guitart M1, González Pérez A7, Hernández I1,2, Ibarria, M1, Lafuente A1, Macias J8, Maroñas O4, Martín E1, Martínez MT6, Marquié M1, Mauleón A1, Monté-Rubio G1, Montrreal L1, Moreno- Grau S1,2, Moreno M1, Orellana A1, Ortega G1,2, Pancho A1, Pelejà E1, Pérez-Cordon A1, Pineda JA8, Preckler S1, Quintela I3, Real LM3,8, Rodríguez-Gómez O1,2, Rosende-Roca M1, Ruiz A1,2, Ruiz S1,2, Sáez ME7, Sanabria A1,2, Santos-Santos MA1, Serrano-Rios M6, Sotolongo-Grau O1, Tárraga L1,2, Valero S1,2, Vargas L1 (1. Research Center and Memory clinic Fundació ACE. Institut Català de Neurociències Aplicades. Universitat Internacional de Catalunya. Barcelona, Spain; 2. CIBERNED, Center for Networked Biomedical Research on Neurodegenerative Diseases, National Institute of Health Carlos III, Ministry of Economy and Competitiveness, Spain; 3. Dep. of Surgery, Biochemistry and Molecular Biology, School of Medicine. University of Málaga. Málaga, Spain; 4. Grupo de Medicina Xenómica, Centro Nacional de Genotipado (CEGEN-PRB3-ISCIII). Universidade de Santiago de Compostela, Santiago de Compostela, Spain; 5. Fundación Pública Galega de Medicina Xenómica- CIBERER-IDIS, Santiago de Compostela, Spain; 6. Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas, CIBERDEM, Spain, Hospital Clínico San Carlos, Madrid, Spain; 7. CAEBI. Centro Andaluz de Estudios Bioinformáticos., Sevilla, Spain; 8. Unidad Clínica de Enfermedades Infecciosas y Microbiología. Hospital Universitario de Valme, Sevilla, Spain). DEGESCO consortium: Adarmes-Gómez AD1,2, Alarcón-Martín E3,4, Álvarez I5, Álvarez V6,7, Amer- Ferrer G8, Antequera M9, Antúnez C9, Baquero M10, Bernal M11, Blesa R2,12, Boada M2,3, Buiza-Rueda D1,2, Bullido MJ2,13,14, Burguera JA10, Calero M2,15,16, Carrillo F1,2, Carrión-Claro M1,2, Casajeros MJ17, Clarimón J2,12, Cruz-Gamero JM4, de Pancorbo MM18, de Rojas I3, del Ser T14, Diez-Fairen M5, Fortea J2,12, Franco E11, Frank-García A2,14,19, García-Alberca JM20, Garcia Madrona S16, Garcia-Ribas G16, Gómez-Garre P1,2, Hernández I2,3, Hevilla S20, Jesús S1,2, Labrador Espinosa MA1,2, Lage C2,21, Legaz A9, Lleó A2,12, López de Munáin A22, López-García S2,21, Macias D1,2, Manzanares S9,23, Marín M11, Marín-Muñoz J9, Marín T20, Marquié M3, Martín Montes A2,13,19, Martínez B9, Martínez C7,24, Martínez V9, Martínez-Lage Álvarez P25, Medina M2,14, Mendioroz Iriarte M26, Menéndez- González M7,27, Mir P1,2, Molinuevo JL28, Monté-Rubio G3, Montrreal L3, Moreno-Grau S2,3, Orellana A3, Pastor AB15, Pastor P5, Pérez Tur J2,29,30, Periñán- Tocino T1,2, Piñol Ripoll G2,31, Rábano A2,15,32, Real de Asúa D33, Rodrigo S11, Rodríguez-Rodríguez E2,21, Royo JL4, Ruiz A2,3, Sanchez del Valle Díaz R34, Sánchez-Juan P2,21, Sastre I2,13, Sotolongo-Grau O3, Tárraga L2,3, Valero S2,3, Vicente MP9, Vivancos L9 (1. Unidad de Trastornos del Movimiento, Servicio de Neurología y Neurofisiología. Instituto de Biomedicina de Sevilla (IBiS), Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, Seville, Spain; 2. www.aging-us.com 9312 AGING CIBERNED, Network Center for Biomedical Research in Neurodegenerative Diseases, National Institute of Health Carlos III, Spain; 3. Research Center and Memory clinic Fundació ACE. Institut Català de Neurociències Aplicades. Universitat Internacional de Catalunya. Barcelona, Spain; 4. Dep. of Surgery, Biochemistry and Molecular Biology, School of Medicine. University of Málaga. Málaga, Spain; 5. Fundació per la Recerca Biomèdica i Social Mútua Terrassa, and Memory Disorders Unit, Department of Neurology, Hospital Universitari Mutua de Terrassa, University of Barcelona School of Medicine, Terrassa, Barcelona, Spain; 6. Laboratorio de Genética Hospital Universitario Central de Asturias, Oviedo; 7. Instituto de Investigación Biosanitaria del Principado de Asturias (ISPA); 8. Department of Neurology, Hospital Universitario Son Espases, Palma, Spain; 9. Unidad de Demencias. Hospital Clínico Universitario Virgen de la Arrixaca; 10. Servei de Neurologia, Hospital Universitari i Politècnic La Fe; 11. Unidad de Demencias, Servicio de Neurología y Neurofisiología. Instituto de Biomedicina de Sevilla (IBiS), Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, Seville, Spain; 12. Memory Unit, Neurology Department and Sant Pau Biomedical Research Institute, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain; 13. Centro de Biologia Molecular Severo Ochoa (C.S.I.C.-U.A.M.), Universidad Autonoma de Madrid, Madrid, Spain; 14. Instituto de Investigacion Sanitaria ‘Hospital la Paz’ (IdIPaz), Madrid, Spain; 15. CIEN Foundation, Queen Sofia Foundation Alzheimer Center, Madrid, Spain; 16. Instituto de Salud Carlos III (ISCIII), Madrid, Spain; 17. Hospital Universitario Ramón y Cajal; Madrid, Spain; 18. BIOMICs, País Vasco; Centro de Investigación Lascaray. Universidad del País Vasco UPV/EHU; 19. Neurology Service, Hospital Universitario La Paz (UAM), Madrid, Spain; 20. Alzheimer Research Center and Memory Clinic. Andalusian Institute for Neuroscience. Málaga, Spain; 21. Neurology Service, Marqués de Valdecilla University Hospital (University of Cantabria and IDIVAL), Santander, Spain; 22. Hospital Donostia de San Sebastían; 23. Fundación para la Formación e Investigación Sanitarias de la Región de Murcia; 24. Servicio de Neurología -Hospital de Cabueñes-Gijón; 25. Centro de Investigación y Terapias Avanzadas. Fundación CITA-alzheimer; 26. Navarrabiomed; 27. Servicio de Neurología -Hospital Universitario Central de Asturias, Oviedo; 28. Barcelona βeta Brain Research Center – Fundació Pasqual Maragall; 29. Unitat de Genètica Molecular. Institut de Biomedicina de València-CSIC; 30. Unidad Mixta de Neurologia Genètica. Instituto de Investigación Sanitaria La Fe; 31. Unitat Trastorns Cognitius, Hospital Universitari Santa Maria de Lleida, Institut de Recerca Biomédica de Lleida (IRBLLeida), Lleida, España. 32. BT-CIEN; 33. Hospital Universitario La Princesa, Madrid, Spain; 34. Hospital Clínic Barcelona). REFERENCES 1. Naj AC, Jun G, Beecham GW, Wang LS, Vardarajan BN, Buros J, Gallins PJ, Buxbaum JD, Jarvik GP, Crane PK, Larson EB, Bird TD, Boeve BF, et al. Common variants at MS4A4/MS4A6E, CD2AP, CD33 and EPHA1 are associated with late-onset Alzheimer’s disease. Nat Genet. 2011; 43:436–41. https://doi.org/10.1038/ng.801 PMID:21460841 2. Proitsi P, Lupton MK, Velayudhan L, Newhouse S, Fogh I, Tsolaki M, Daniilidou M, Pritchard M, Kloszewska I, Soininen H, Mecocci P, Vellas B, Williams J, et al, Alzheimer’s Disease Neuroimaging Initiative, and GERAD1 Consortium. 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Ikram MA, Brusselle G, Ghanbari M, Goedegebure A, Ikram MK, Kavousi M, Kieboom BC, Klaver CC, de Knegt RJ, Luik AI, Nijsten TE, Peeters RP, van Rooij FJ, et al. Objectives, design and main findings until 2020 from the Rotterdam Study. Eur J Epidemiol. 2020; 35:483–517. https://doi.org/10.1007/s10654-020-00640-5 PMID:32367290 23. Reiman EM, Webster JA, Myers AJ, Hardy J, Dunckley T, Zismann VL, Joshipura KD, Pearson JV, Hu-Lince D, Huentelman MJ, Craig DW, Coon KD, Liang WS, et al. GAB2 alleles modify Alzheimer’s risk in APOE epsilon4 carriers. Neuron. 2007; 54:713–20. https://doi.org/10.1016/j.neuron.2007.05.022 PMID:17553421 www.aging-us.com 9315 AGING Supplementary Figures Supplementary Figure 1. Stage I meta-analysis manhattan and QQ plots (all strata). Supplementary Figure 2. Stage II meta-analysis manhattan and QQ plots (all strata). www.aging-us.com 9316 AGING Supplementary Figure 3. Stage III meta-analysis manhattan and QQ plots (all strata). Supplementary Figure 4. Stage IV meta-analysis manhattan and QQ plots (all strata). www.aging-us.com 9317 AGING Supplementary Figure 5. Stage I-IV meta-analysis manhattan and QQ plots (all strata). Supplementary Figure 6. Sex stratified meta-analysis manhattan and QQ plots. www.aging-us.com 9324 AGING Supplementary Figure 13. Cortex snRANseq heatmap for RRA candidates shared by the three APOE strata: (A) Unstratified, (B) APOE2, (C) APOE3, (D) APOE4 AD cases vs controls. www.aging-us.com 9325 AGING Supplementary Figure 14. Cortex snRANseq heatmap for APOE2 RRA cortex candidates. www.aging-us.com 9326 AGING Supplementary Figure 15. Cortex snRANseq heatmap for APOE3 RRA cortex candidates. www.aging-us.com 9327 AGING Supplementary Figure 16. Cortex snRANseq heatmap for APOE4 RRA cortex candidates. www.aging-us.com 9328 AGING Supplementary Tables Please browse Full Text version to see the data of Supplementary Tables 1–39. Supplementary Table 1. Stage I-III APOE2 stratified meta-analysis results (p<10-4). Supplementary Table 2. Stage I-III APOE3 stratified meta-analysis results (p<10-4). Supplementary Table 3. Stage I-III APOE4 stratified meta-analysis results (p<10-4). Supplementary Table 4. Stage I-IV APOE2 stratified meta-analysis results (p<10-4). Supplementary Table 5. Stage I-IV APOE3 stratified meta-analysis results (p<10-4). Supplementary Table 6. Stage I-IV APOE4 stratified meta-analysis results (p<10-4). Supplementary Table 7. Sex stratified meta-analysis (males). Supplementary Table 8. Sex stratified meta-analysis (females). Supplementary Table 9. APOE and sex stratified analysis: APOE2 males meta-analysis results (p<10-4). Supplementary Table 10. APOE and sex stratified analysis: APOE3 males meta-analysis results (p<10-4). Supplementary Table 11. APOE and sex stratified analysis: APOE4 males meta-analysis results (p<10-4). Supplementary Table 12. APOE and sex stratified analysis: APOE2 females meta-analysis results (p<10-4). Supplementary Table 13. APOE and sex stratified analysis: APOE3 females meta-analysis results (p<10-4). Supplementary Table 14. APOE and sex stratified analysis: APOE4 females meta-analysis results (p<10-4). Supplementary Table 15. Stage I-III APOE2 stratified meta-analysis gene level results. Supplementary Table 16. Stage I-III APOE3 stratified meta-analysis gene level results. Supplementary Table 17. Stage I-III APOE4 stratified meta-analysis gene level results. Supplementary Table 18. Stage I-III stratified gene level meta-analysis results (Stage I-II) for known AD genes (all strata). Supplementary Table 19. Differential expression analysis in blood: APOE2 stratum. Supplementary Table 20. Differential expression analysis in blood: APOE3 stratum. Supplementary Table 21. Differential expression analysis in blood: APOE4 stratum. Supplementary Table 22. Differential expression analysis in cortex: APOE2 stratum. Supplementary Table 23. Differential expression analysis in cortex: APOE3 stratum. Supplementary Table 24. Differential expression analysis in cortex: APOE4 stratum. www.aging-us.com 9329 AGING Supplementary Table 25. RRA integrative analysis in blood: APOE2 stratum. Supplementary Table 26. RRA integrative analysis in blood: APOE3 stratum. Supplementary Table 27. RRA integrative analysis in blood: APOE4 stratum. Supplementary Table 28. RRA integrative analysis in cortex: APOE2 stratum. Supplementary Table 29. RRA integrative analysis in cortex: APOE3 stratum. Supplementary Table 30. RRA integrative analysis in cortex: APOE4 stratum. Supplementary Table 31. RRA integrative analysis in blood: enrichment analysis APOE2 stratum. Supplementary Table 32. RRA integrative analysis in blood: enrichment analysis APOE3 stratum. Supplementary Table 33. RRA integrative analysis in blood: enrichment analysis APOE4 stratum. Supplementary Table 34. RRA integrative analysis in cortex: enrichment analysis APOE2 stratum. Supplementary Table 35. RRA integrative analysis in cortex: enrichment analysis APOE3 stratum. Supplementary Table 36. RRA integrative analysis in cortex: enrichment analysis APOE4 stratum. Supplementary Table 37. Blood proteomics differential expression results (all strata). Supplementary Table 38. Cortex proteomics meta-analysis (all strata). Supplementary Table 39. Cortex snRNAseq differential expression analysis.